Micro-fluidic mixer and method of determining pathogen inactivation via antimicrobial solutions

ABSTRACT

A sample of produce wash water containing an antimicrobial sanitizer fluid, and a reference pathogen fluid are both injected into a pathogen inactivation region of a micro-fluidic mixer. The produce wash water (i.e. sanitizer fluid/pathogen fluid mix) is directed through mixer elements in the pathogen inactivation region of the micro-fluidic mixer. In the sanitizer deactivation region, a sanitizer deactivation solution is added to the sanitizer fluid/pathogen fluid mix to produce a deactivated solution. The deactivated solution is evaluated for the presence of the pathogen and the characteristics of the sanitizer. In the preferred embodiment, the sanitizer comprises chlorine and the pathogen comprises  E. coli  bacteria.

REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/048,507, filed Sep. 10, 2014, which is incorporated herein byreference in its entirety.

FIELD OF THE INVENTION

The disclosed method and apparatus relates to pathogen inactivationkinetics in a solution. Specifically, the method and apparatus describedherein relates to a means of determining whether sufficient freechlorine is present in a wash solution to inactivate a target pathogen.

BACKGROUND OF THE INVENTION

Since the 1996 Escherichia coli (E. coli) O157:H7 outbreak, when nearly10,000 people were sickened, foodborne illness has become a more visiblethreat to the public health. Almost two decades later, microbialcontamination remains one of the most serious challenges for assuringthe safety of food supplies. In 2011, the Centers for Disease Controland Prevention (CDC) estimated that roughly 48 million people aresickened by food-borne pathogens each year, including 3,000 cases endingin deaths.

Among all food categories, fruits and vegetables have emerged as themost significant vector of food borne bacterial pathogens because theyare frequently consumed raw. Washing is an important step duringfresh-cut produce processing as it removes the debris, soils, andproduce latex released from the cut edges and maintains quality andshelf life of the final products, and can reduce 1-2 log cfu/g microbialloads.

Maintaining a high level of sanitizer in wash water is a practicalchallenge to the produce industry due to the rapid reaction of organicmatter with sanitizers, especially the widely used hypochlorous acid(chlorine). As a result of its reaction with organic materials presentin the wash water, free chlorine concentration usually declines rapidlyduring fresh produce wash operations.

Determination of the minimum free chlorine concentration needed toprevent pathogen survival/cross-contamination during produce washing isessential for the development of science-based food safety regulationsand practices. Although the trend of chlorine concentration-contact timeon pathogen inactivation is generally understood, specific informationon chlorine and the kinetics of pathogen inactivation (particularly atless than 1 second) is urgently needed by the produce processingindustry. However, conventional approaches to obtain this critical datahave been unable to adequately measure very rapid responses.

The need exists for a quick and accurate means of determining theadequacy of chlorine wash solutions. The current disclosure is directedto a novel micro-fluidic device that is able to make the requireddetermination in times as short as 0.1 second.

The micro-fluidic mixer described herein comprises one inlet each forbacterial, chlorine and dechlorinating solutions, and one outlet foreffluent collection. To determine the kinetics of free chlorine onpathogen inactivation, chlorine solutions of varying concentrations arepumped into the micro-fluidic mixer. A sample bacterial solution isinjected into the mixer through a separate inlet.

After mixing, a dechlorinating solution is injected into the mixer tostop the chlorine-pathogen reaction. The effluent is collected and thesurviving bacteria cells are enumerated using a modified ‘Most ProbableNumber’ method. Free chlorine concentration is determined using astandard colorimetric method. The contact time is precisely controlledby adjusting the solution flow rate and quantitatively determined bycomputational fluid dynamics modeling.

SUMMARY OF THE INVENTION

This disclosure is directed to a micro-fluidic sanitizer analysissystem. The system comprises a micro-fluidic mixer with a pathogeninactivation region, and a sanitizer inactivation region. The system isstructured so that a sanitizer fluid mixes with and at least partiallyinactivates a reference pathogen fluid in the pathogen inactivationregion. The sanitizer fluid/pathogen fluid mix is then directed to thesanitizer inactivation region, where the sanitizer is inactivated toproduce an inactivated sanitizer fluid. The inactivated sanitizer fluidis analyzed for a presence of the pathogen and characteristics of theinactivated sanitizer fluid.

The disclosure is further directed to a method of mixing a sanitizersolution with a reference pathogen fluid. In accordance with the method,a sanitizer fluid is injected into a sanitizer fluid inlet, and apathogen fluid is directed into a pathogen fluid inlet. The sanitizerfluid and the pathogen fluid converge at a first y-injection mixer sothat the first y-injection mixer mixes the sanitizer fluid and thepathogen fluid. The sanitizer fluid/pathogen fluid mix then flows into afirst Dean's vortex mixer. The sanitizer fluid/pathogen fluid mix isthen directed into a second y-injection mixer where it is blended with asanitizer deactivation fluid to create a deactived fluid. Thedeactivated fluid then flows into a second Dean's vortex mixer. Afterthe second Dean's vortex mixer the deactivated fluid flows out of themicro-fluidic mixer where it is analyzed for the presence of thepathogen and the characteristics of the sanitizer fluid.

BRIEF DESCRIPTION OF THE DRAWINGS

This patent or application file contains at least one drawing executedin color. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1 is an elevational schematic of the micro-fluidic mixer describedherein.

FIG. 2 is a (black and white) photograph of the micro-fluidic mixerdescribed herein.

FIG. 3 shows the chlorine concentration and bacterial suspension duringa test period.

FIG. 4 is a schematic view of the computational fluid dynamics modeledmixing of bacteria, chlorine, and neutralizing solutions inside themicro-fluidic mixer (described herein) at a flow rate equivalent to 0.75contact time.

FIG. 5 is a graph showing the correlation between volumetric flow rateand contact time.

FIG. 6 is a graph showing the effects of chlorine concentration andcontact time on log reductions of E. coli.

FIG. 7 is a graphic comparison of experimental test results to threeselected bacterial inactivation models.

FIG. 8 is a contour graph of log reductions (1 to 5 log) of E. coli atdifferent chlorine concentrations and contact times.

FIG. 9 is a decimal reduction time (D-value) required at certain freechlorine concentrations to achieve 90% (1-log reduction) of E. coli inwater.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

As generally shown in FIG. 1, the current invention is directed to amicro-fluidic mixer 10. The micro-fluidic mixer 10 comprises two mixingregions 20, 30. The first region generally comprises a bacterialpathogen inactivation region 20, and the second region comprises adechlorination region 30.

As shown in FIG. 1, the pathogen inactivation region 20 is designed tomix a sample of produce wash water with a reference bacterial source.The combined wash water and bacterial solution leaving the pathogeninactivation region 20 is directed into the dechlorination region 30. Indechlorination region 20, a chlorine inactivating solution (i.e. adechlorinating solution) is added to the fluid mix to inactivate thechlorine in the solution. From the dechlorination region 30, theeffluent flows out an outlet 40. The effluent leaving the outlet 40 isexamined to determine the quantity of surviving bacteria cells in theeffluent.

Specifically, as shown in FIG. 1, the pathogen inactivation region 20comprises a first inlet 21 and a second inlet 22. A bacterial referencesample solution is injected into the first inlet 21. In the preferredembodiment, the bacterial sample solution comprises Escherichia coli(i.e. E. coli) O157:H7 (RM4406, ATCC 700728), but other types ofbacteria or contaminant should be considered within the scope of theinvention. A sample of produce wash water (or other studied solutions)is injected in the second inlet 22. The dashed-line arrows shown in FIG.1 indicate the direction of fluid flow.

The bacterial fluid and the wash water converge at a Y injection-mixer24. The fluid is then directed though a Dean's vortex-mixer 26configuration to thoroughly mix the wash water and bacterial fluids. TheDean's vortex mixer 26 includes chaotic mixer elements 28 that extendinto the mixer's 28 main channels and create turbulence that furthermixes the fluids.

As the fluid leaves pathogen inactivation region 20 and enters thedechlorination region 30, the fluid flows into a second y-injectionmixer 31. In the (second) y-injection mixer 31 the fluid is mixed with adechlorination fluid entering from a dechlorination fluid inlet 32. Thedechlorination fluid is mixed with the bacteria/wash water mix in a(second) Deans vortex 34 which also includes a (second) set of chaoticmixer elements 36. The (now) dechlorinated fluid flows out the outlet 40where any surviving bacteria are analyzed.

FIG. 2 shows a picture of the micro-fluidic mixer 10, as fabricated. Thedevice was fabricated and assembled using modified microfabricationmethods that are well known in the art. The master mold was fabricatedon a silicon wafer with SU-8 microchannels via contact photolithographyusing a mask aligner according to the supplier's instruction.Polydimethylsiloxane (PDMS) replicas with patterned microchannels wereprototyped via soft lithography by curing the PDMS mixture (base: curingagents=10:1) on the mast mold at 125° C. for 20 minutes. Cured replicaswere further assembled into the micro-fluidic mixer on a clean siliconwafer via O₂ plasma treatment (March Jupiter III, Westlake, Ohio, USA).

FIG. 2 shows the bacteria solution port 21, wash water solution port 22,and the dechlorination solution 32 port, as well as the effluent outletport 40. The inlet ports 21, 22, 32, and the outlet port 40 were createdusing Nanoports (IDEX Health and Science, Oak Harbor, Wash., USA) toprovide seamless connection with an external syringe pump, which allowsadjustable flow rate.

Upon successful assembly of the device 10, the micro-fluidic mixer 10was initially evaluated for its effectiveness and accuracy in providinga constant, stable input of solutions through the microchannel. Thestability of bacterial concentration was tested by pumping the bacterialsuspension (via bacteria inlet 21, FIGS. 1 and 2) and sterile water (viainlet wash water inlet 22 and dechlorination fluid inlet 32, FIGS. 1 and2) at different flow rates. The effluent was collected and the bacteriawere enumerated using modified ‘Most Probable Number’ (MPN) proceduresas described in the following section. The chlorine stability was testedsimilarly by pumping chlorine solution (via wash water inlet 22) withwater, and the effluent was collected to quantify chlorine concentrationusing the DPD colorimetric method. In the follow-up chlorine stabilitytest and dechlorinating efficiency studies, both chlorine (via washwater inlet 22) and dechlorinating solutions (via dechlorination inlet32) were pumped into the micro-fluidic mixer with water. The effluentwas collected to detect chlorine residual using the DPD method.

The mixing efficiency and mixing patterns were evaluated by modelingscalar mixing (mixing efficiency) using the Computational Fluid Dynamicssoftware (Autodesk Simulation CFD, 2014). The chlorine diffusioncoefficient in water used in this modeling was 1.38×10⁻⁵ cm²/s. Thesolution has a low Reynolds number in this device, and the liquid wasdetermined to be incompressible and the flow was characterized aslaminar in the scalar mixing model. The boundary conditions (i.e. inletvolume flow rate, and outlet pressure) were adjusted to reflect theactual experimental value.

The contact time between bacteria and chlorine solution was defined asthe time required to pass through the pathogen deactivation region 20(FIG. 1). The contact time was precisely controlled by adjusting theflow rate of the syringe pump that supplied the fluid to themicro-fluidic mixer 10. The mixing efficiency (scalar mixing) of thedevice and contact time distribution also were determined quantitativelyusing the trace function in the CFD software.

Pathogen Inactivation Study

A series of chlorine solutions with free chlorine concentrations of 0.5,1, 5, 10, 20, 50 mg/L were freshly prepared using sodium hypochlorite.The free chlorine concentration before and after tests were verifiedusing chlorine photometer (CP-15, HF Scientific Inc., Ft. Myers, Fla.,USA). To determine the kinetics of chlorine on pathogen inactivation,chlorine solutions were pumped into the micro-fluidic mixer, togetherwith the bacterial suspension of E. coli O157:H7 through a separateinlet. This was followed by injection of dechlorinating solution at theentry to Region B to stop the chlorine-pathogen reaction. To consider adilution effect inside the micro-fluidic mixer (pathogen deactivationregion 20, FIG. 1), the chlorine concentration in the start solution wasdouble the effective concentration in the microchannel. Allconcentrations discussed in the study are effective concentrations afterconsidering the dilution factors.

The dechlorinating solution containing 0.1% sodium pyruvate anddechlorinating reagent (containing 1˜10% sodium thiosulfate and 90˜99%sodium sulfate) was pumped via the dechlorination inlet 32 (FIG. 1) tostop the inactivation process. The contact time was controlled preciselyby adjusting the solution flow rate and was determined quantitatively byCFD modeling. Pathogen survival was quantified using a modified ‘MostProbable Number’ method as detailed in the inventors' previouspublications.

All experiments were repeated at least three times, and data wasanalyzed using SAS software (Version 9.3). E. coli O157:H7 populationswere subjected to log transformation before statistical analysis. Datawas analyzed as a two-way analysis of variance (ANOVA) with treatmentand contact time as the main factors. Assumptions of normality andvariance homogeneity were checked and the variance grouping techniquewas used to correct for variance heterogeneity. When effects werestatistically significant, means comparisons were done using Tukey'srange test with adjusted p-values to maintain experiment-wise error of≦0.05.

Results

The effectiveness of the micro-fluidic mixer in maintaining stableconcentrations of chlorine and bacterial suspensions during pumping wasvalidated. The stability of the solution delivery first was tested byseparately pumping chlorine (via wash water inlet 22, FIG. 1) withwater, and then bacterial suspension (bacteria inlet 21, FIG. 1) withwater, and collecting the effluent of each independent test forquantification. As shown in FIG. 3, both chlorine concentration (5 mg/L)and bacterial suspension (10⁷ MPN/mL) were stable for the entire 20minute test period during continuous pumping at a flow rate of 0.16mL/minute, or the equivalent of a 0.75 second contact time. Thedeviation of chlorine concentration was no more than 3%. E. coli O157:H7concentrations were less than 2.5% (FIG. 3) from the respectivetheoretical values.

The mixing efficiency of the pathogen inactivation region 20 and thedechlorination region 30 (FIG. 1) was also validated in separatedexperiments. The total pathogen inactivation (negative control) wasvalidated by mixing chlorine (50 mg/L) and pathogen solution (10⁸MPN/mL) inside the micro-fluidic mixer without the addition ofdechlorinating solution, resulting in no bacterial survival detectedfrom the collected effluent. The neutralizing power of dechlorinatingreagent was demonstrated when pumping both chlorine and dechlorinatingsolutions into the micro-fluidic mixer, which resulted in no chlorineresidue being detected in the collected effluent. These results areconsistent with other reported micro-fluidic mixer studies withdifferent solutions and flow rates.

The mixing efficiency and mixing pattern were studied using CFD modelingof scalar mixing. During the modeling process, all solutions wereconsidered as Newtonian fluids with non-slip boundary conditions. Thesolution has a low Reynolds number and laminar flow characteristics, dueto the small scale. Achieving efficient and rapid mixing is one of thechallenging tasks in micro-fluidic mixers, because mixing under laminarflow conditions depends on slow diffusion of chemicals and bacterialcells.

Therefore, the study incorporated multiple passive micro-fluidic mixerdesigns that can be completed by simple modification of channelgeometries into the following configurations: “Y” injection, serpentinechannels, and channels with patterned grooves. These modifications anddesigns enhance folding, stretching and breaking of the laminar flows,which improves the mixing efficiency during flow. FIG. 4 shows themodeled scalar mixing of chlorine, bacteria, and dechlorinatingsolutions in our micro-fluidic mixer.

The modeled result indicated that mixing can be achieved very rapidlyand efficiently in the inventors' design. Therefore, the micro-fluidicmixer provides the means to conduct studies on pathogen inactivationkinetics in time periods as brief as 0.1 second. The CFD modeling alsoindicated that increased flow rates which result in decreased contacttime, could increase mixing efficiency.

The micro-fluidic mixer was also optimized for the adjustability of flowrate and contact time. By changing flow rate at different inlets, thetime needed for fluid elements to pass through the mixing region can bevaried. The relationship between the initial flow rate required and thespecific contact time distribution can also be determined and predictedby the trace function in the CFD modeling software. FIG. 5 shows thecorrelation between inlet flow rate and contact time distribution. Themicro-fluidic mixer had a channel dimension of 1 mm (width) by 0.1 mm(height), resulting in a cross-sectional area (A) of 0.1 mm². The traveldistance (D) of the chaotic mixer was 10 mm. The contact time (t) can beadjusted by changing the flow rate (r), and is calculated as:

t=D×A/r   (1)

Ideally, every fluid element entering mixing the pathogen deactivationregion 20 (FIG. 1) should spend the same amount of time before it entersthe dechlorination region 30 (FIG. 1). However, with the folding,stretching, and breaking of the laminar flow condition, different fluidelements took different amounts of time to pass through the mixingpatterns. In reality, not all fluid elements spend the same amount oftime passing through the mixing regions in the micro-fluidic mixer.However, the variation did not affect the precision of contact timecontrol with the coefficient of variation (C_(V)) <0.05. The calculatedflow rate was adopted in the following pathogen inactivation study tocontrol the contact time from 0.1 to 1.5 seconds.

The effect of chlorine concentration and exposure time on E. coliO157:H7 inactivation was tested using the micro-fluidic mixer aftervalidation tests were completed. As shown in FIG. 6, the inactivation ofE. coli O157:H7 was significantly affected by free chlorineconcentration (P<0.0001), exposure time (P<0.0001), and theirinteractions (P<0.0001). The chlorine concentration and contact timeexhibited a negative relationship, i.e., more concentrated chlorinesolution required less contact time to achieve a 5-log reduction. Withan E. coli O157:H7 suspension solution containing 1×10⁸ CFU/mL, 1.0 mg/Lfree chlorine solutions achieved 0.14 log reduction in 0.25 second, and4.97 log reduction in 1.00 second. Increasing chlorine concentrationsignificantly reduced exposure time required to achieve the same logreduction. With a free chlorine concentration of 10.0 mg/L, a 3.87 logreduction was achieved in 0.1 second, and a 5.77 log reduction in 0.25second.

The determination of minimum effective free chlorine concentrationrequired to prevent pathogen survival and cross-contamination duringproduce wash is a major challenge facing the produce industry andregulatory agencies. The US industry-wide hazard analysis and criticalcontrol points (HACCP) program has set 1 mg/L free chlorine as thecritical control limit (CCP) previously. Recent studies have shown thatthis chlorine concentration is insufficient in preventing pathogencross-contamination.

However, the effective minimum free chlorine concentration to preventcross-contamination has not yet been determined and validated. Duringwater-mediated cross-contamination, pathogens are first dislodged fromthe surface of the contaminated produce, survive in wash water, and thentransfer to the originally uncontaminated produce as they move alongwith wash water. Therefore, the effective minimum free chlorineconcentration required to prevent pathogen cross-contamination must be aconcentration that can inactivate pathogens instantaneously. Theinventors determined that a free chlorine concentration of 1 mg/L willachieve a 5-log reduction in 1.00 second, 5 mg/L in 0.50 second, and 10and 20 mg/L in 0.25 second. Chlorine concentration at 0.5 mg/L and 50mg/L were included in the study, but the accurate determination ofexposure time required to achieve a 5-log reduction requires additionalmodification of the micro-fluidic mixer to accommodate such low and highchlorine concentrations.

The results obtained with 1, 5, 10, and 20 mg/L in this study provide areasonably good explanation for the observation that cross-contaminationoccurs at 1 mg/L, but not at 10 mg/L, as 1 mg/L requires more than 1.2seconds to achieve a 5-log reduction in pathogen while 10 mg/L killspathogens much faster (0.2 second for a 5-log reduction). The resultswere analyzed with the contour graph (FIG. 7), with the predicted logreduction (1-5 log) at different chlorine concentrations and contacttimes.

Kinetic Models

Kinetic modeling was also applied to simplify the complicateddisinfection phenomena of produce wash system. Three models that arecommonly used for studying bacterial disinfection kinetics withchemical-based disinfectant, including the Chick-Watson Model, the HomModel, and the Selleck Model.

Watson developed an empirical logarithmic equation to relate theinactivation constant (k) to disinfectant concentration (C) and reactiontime (t):

$\begin{matrix}{{\log \left( \frac{N}{N_{0}} \right)} = {{- {kC}^{n}}t}} & (2)\end{matrix}$

where N=number of pathogen cells per unit volume,

N₀=number of pathogen cells initially at time zero,

k=strain- and condition-specific inactivation constant,

C=free chlorine concentration,

n=coefficient of dilution,

t=reaction (contact) time.

From the experimental data in FIG. 6, k and n were determined as:

$\begin{matrix}{{\log \left( \frac{N}{N_{0}} \right)} = {{- 3.48}C^{0.24}t}} & (3)\end{matrix}$

The Hom model is a generalized empirical equation of the Chick-Watsonmodel considering chlorine disinfection as a pseudo first-orderreaction:

$\begin{matrix}{{\log \; \left( \frac{N}{N_{0}} \right)} = {{- {kC}^{n}}t^{m}}} & (4)\end{matrix}$

where m is a reaction rate constant and other factors are as describedabove for equation 2. The constants were modeled using experimental dataas:

$\begin{matrix}{{\log \left( \frac{N}{N_{0}} \right)} = {{- 3.18}C^{0.28}t^{0.18}}} & (5)\end{matrix}$

The Selleck model was originally developed to predict chlorineinactivation of bacteria in wastewater. The model was empirical and canbe adjusted to different sanitization systems:

$\begin{matrix}{{\log \left( \frac{N}{N_{0}} \right)} = {{- n}\; {\log \left( {1 + \frac{Ct}{k}} \right)}}} & (6)\end{matrix}$

The experimental data also was applied to compute values of empiricalcoefficient k and n, which yields:

$\begin{matrix}{{\log \left( \frac{N}{N_{0}} \right)} = {{- 0.678}\; {\log \left( {1 + \frac{Ct}{0.25}} \right)}}} & (7)\end{matrix}$

The pathogen inactivation kinetics of the three selected models werecompared statistically with the original experimental data. FIG. 8 showsthe effect of chlorine concentration at a specific contact time (0.25second). Both the Watson (P=0.860) and Hom (P=0.841) models reflectedthe pathogen inactivation scenarios with short contact time. The Selleckmodel was used to predict pathogen survival in chlorine over asubstantially longer contact time (e.g. 3 hours) than was used in thestudies reported here. Thus it is not surprising that the model didn'tfit the results from this short time-course kinetic study (P<0.0001).The variation and difference between experimental data and modeledresults could be explained by the heterogeneity of bacterial cells in apopulation relative to their inherent resistance to chlorine, which wasdistributed in a spatially- or time-dependent manner.

In a chlorine-based disinfection process, the decimal reduction time(D-value) is defined as the time required at a specified free chlorineconcentration to kill 90% (one log reduction) of the organisms beingstudied. The D-value here was calculated based on the empirical modelsdeveloped in this study, namely the Watson model (equation 3) and theHom model (equation 5). FIG. 9 shows the relationship between chlorineconcentration and D-value. For the widely used 1 mg/L free chlorineconcentration in HACCP programs, the D-value is 28.06 milliseconds. Byincreasing chlorine concentration to 5, 10, and 20 mg/mL, the D-valuereduces to 5.72, 2.87, and 1.43 milliseconds respectively.

Conclusions

A micro-fluidic mixer useful for assessing pathogen inactivationkinetics at less than 1 second was designed, fabricated, and validated.This device also was used to determine the time and dose dependentresponse of pathogen inactivation via free chlorine. Test resultsindicate that 1) E. coli O157:H7 inactivation is significantly affectedby free chlorine concentration (P<0.0001), contact time (P<0.0001), andtheir interactions (P<0.0001); 2) A 5-log reduction of E. coli O157:H7requires exposing E. coli O157:H7 cells to a solution containing 1 mg/Lfree chlorine for at least 1.2 seconds, or a solution containing 10 mg/Lfree chlorine for 0.2 second. These findings provide criticalinformation regarding the determination of minimum free chlorineconcentration required to prevent pathogen survival andcross-contamination during fresh produce wash operations.

For the foregoing reasons, it is clear that the method and apparatusdescribed herein provides an innovative micro-fluidic mixer fordetermining whether sufficient free chlorine is present in a washsolution to inactivate a target pathogen. The disclosed method andapparatus may be modified and customized as required by a specificoperation or application, and the individual components may be modifiedand defined, as required, to achieve the desired result.

Although all of the materials of construction are not described, theymay include a variety of compositions consistent with the functiondescribed herein. Such variations are not to be regarded as a departurefrom the spirit and scope of this disclosure, and all such modificationsas would be obvious to one skilled in the art are intended to beincluded within the scope of the following claims.

What is claimed is:
 1. A micro-fluidic sanitizer analysis system, thesystem comprising: a micro-fluidic mixer, the micro-fluidic mixercomprising: (a) a sanitizer fluid inlet for injecting a sanitizer fluid;(b) a pathogen fluid inlet for injecting a pathogen fluid; (c) a firsty-injection mixer positioned to receive and mix the target sanitizerfluid and the pathogen fluid; (d) a first Dean's vortex mixer positionedto receive the sanitizer fluid/pathogen fluid mix from the firsty-injection mixer; (e) a sanitizer deactivation fluid inlet; (e) asecond y-injection mixer positioned to receive and blend the sanitizerfluid/pathogen fluid mix, and the sanitizer deactivation fluid, theblended fluids comprising a deactivated fluid; (f) a second Dean'svortex mixer positioned to receive and further mix the deactivatedfluid; (g) a micro-fluidic mixer outlet positioned so that thedeactivated fluid is expelled from the micro-fluidic mixer.
 2. Thesystem of claim 1 wherein the sanitizer comprises a chlorine producewash solution.
 3. The system of claim 1 wherein the reference pathogencomprises E. coli.
 4. The system of claim 1 wherein the Dean's vortexmixer comprises chaotic mixer elements.
 5. A micro-fluidic sanitizeranalysis system, the system comprising a micro-fluidic mixer having apathogen inactivation region, and a sanitizer deactivation region; thesystem being structured so that the system causes a sanitizer fluid tomix with and at least partially inactivate a pathogen fluid in thepathogen inactivation region, and then the sanitizer is deactivated inthe sanitizer deactivation region to produce a deactivated sanitizerfluid, the deactivated sanitizer fluid then being analyzed.
 6. Themicro-fluidic system of claim 5 wherein the system is structured so thatthe pathogen inactivation region comprises a y-injection structure. 7.The micro-fluidic system of claim 5 wherein the system is structured sothat the pathogen inactivation region comprises at least one serpentinechannels.
 8. The micro-fluidic system of claim 5 wherein the system isstructured so that the pathogen inactivation region comprises at leasttwo channel segments with patterned grooves.
 9. The micro-fluidic systemof claim 5 wherein the system is structured so that the pathogeninactivation region comprises at least one Dean's mixer.
 10. Themicro-fluidic system of claim 5 wherein the sanitizer fluid comprises achlorine produce wash solution.
 11. The micro-fluidic system of claim 5wherein the pathogen fluid comprises E. coli.
 12. The micro-fluidicsystem of claim 5 wherein the system comprises a fluid channel, thechannel of the pathogen inactivation region having the same structure asthe structure of the sanitizer deactivation region.
 13. Themicro-fluidic system of claim 5 wherein the system is structured so thatthe sanitizer deactivation region comprises a y-injection structure. 14.The micro-fluidic system of claim 5 wherein the system is structured sothat the sanitizer deactivation region comprises at least one serpentinechannels.
 15. The micro-fluidic system of claim 5 wherein the system isstructured so that the sanitizer deactivation region comprises at leasttwo channel segments with patterned grooves.
 16. The micro-fluidicsystem of claim 5 wherein the system is structured so that the pathogeninactivation region comprises at least one Dean's mixer.
 17. Themicro-fluidic system of claim 1 wherein the deactivated sanitizer fluidis analyzed for a presence of the pathogen and characteristics of thedeactivated sanitizer fluid.
 18. A method of mixing a sanitizer solutionwith a reference pathogen fluid, the method comprising: (a) injecting asanitizer into a sanitizer fluid inlet; (b) injecting a pathogen fluidinto a pathogen fluid inlet; (c) directing the sanitizer fluid and thepathogen fluid into a first y-injection mixer so that the firsty-injection mixer mixes the sanitizer fluid and the pathogen fluid; (d)directing the sanitizer fluid/pathogen fluid mix into a first Dean'svortex mixer; (e) directing the sanitizer fluid/pathogen fluid mix intoa second y-injection mixer and blending the sanitizer fluid/pathogenfluid mix with a sanitizer deactivation fluid to create deactived fluid;(f) directing the deactivated fluid mix into a second Dean's vortexmixer; (g) analyzing the deactivated fluid.
 19. The method of claim 18wherein, in step (a), the sanitizer comprises chlorine.
 20. The methodof claim 18 wherein, in step (b), the pathogen fluid comprises bacteria.